Survey over image thresholding techniques and quantitative performance evaluation

By Mehmet Sezgin, Bülent Sankur. J. Electronic Imaging, 2004

5,172 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4dafbbc0cd8eb3e9967dd6b8bfce216a4dd3c1fc

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In Computer Vision and Pattern Recognition

Citations in the corpus

A New Local Adaptive Thresholding Technique in Binarization (2012, methodology): “Mehmet Sezgin and Sankur [26] presented a comparative analysis of these methods and found that Sauvola’s method is the best on non documental images, but somewhat poor in document images.”
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction (2020, methodology): “Generally, a thresholding method separates changes from unchanges by ﬁnding an optimal threshold such as Kittler and Illingworth minimum-error thresholding algorithm [7], Otsu’s algorithm [16].”
Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database (2013, methodology): “These methods assume that the image can be formally characterised by mixture densities of foreground and background pixels [37] In this equation, p ( g ), g = 0, … , G , where G is the maximum luminance value in the image, is referred to as a probability mass function (PMF).”
Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images (2010, background): “Several approaches have been presented in the literature, and a survey on image thresholding methods can be found in [31].”
Opposition-Based Differential Evolution (2008, methodology): “To compare two binary images, Misclassification Error (ME) [Sezgin and Sankur 2004; Yasnoff et al. 1977] can be a reasonable and straightforward measure to use.”
Recent Progress in Diagnosing the Reliability of Electrical Equipment by Using Infrared Thermography (2012, background): “There are various thresholding techniques for image segmentation that have been proposed and published [46].”
Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects1[C][W] (2012, methodology): “For a more detailed description on automatic threshold methods, we refer to Otsu (1979), Abràmoff et al. (2004), and Sezgin and Sankur (2004).”
Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “…we will examine hysteresis segmentation [Vogel and Kretzschmar, 1996], indicator kriging [Oh and Lindquist, 1999], converging active contours [Sheppard et al., 2004], watershed segmentation [Vincent and Soille, 1991], and Bayesian Markov random field segmentation [Kulkarni et al., 2012].”
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm (2006, methodology): “Several methods have been proposed to binarize an image, Sezgin and Sankur (2004).”
MRI Segmentation of the Human Brain: Challenges, Methods, and Applications (2015, background): “A survey on thresholding techniques is provided in [63].”
A survey of graph theoretical approaches to image segmentation (2013, background): “The reviews of various segmentation techniques can be found for image thresholding methods [24], medical image segmentation [25,26], statistical level set segmentation [27], 3D image segmentation [28], edge detection techniques [29] and so on.”
Image analysis algorithms for estimating porous media multiphase flow variables from computed microtomography data: a validation study (2010, background): “, [32, 34, 35, 38, 48]), and Sezgin and Sankur [47] provide a comprehensive survey of 40 segmentation methods.”
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks (2016, methodology): “The global threshold value (T ) of similarity is gotten by iterative threshold method [44].”
On the use of spatter signature for in-situ monitoring of Laser Powder Bed Fusion (2017, methodology): “In [48] several existing thresholding methods were divided into the following categories: histogram-based, clusteringbased, entropy-based, attribute-based, spatial and local approaches.”
Automatic thresholding for defect detection (2004, methodology): “In-depth survey and evaluation of various thresholding methods are given by Sahoo et al. (1988), Lee et al. (1990), Glasbey (1993) and more recently, by Sezgin and Sankur (2004).”
A survey of GPU-based medical image computing techniques. (2012, neutral): “Local and global threshold methods, based on target image intensity distribution, have also been widely used in image segmentation (37).”